Using ART2 networks to deduce flow velocities

被引:5
|
作者
Jambunathan, K
Fontama, VN
Hartle, SL
AshforthFrost, S
机构
[1] Department of Mechanical Engineering, Nottingham Trent University, Nottingham NG1 4BU, Burton Street
来源
关键词
artificial neural networks (ANNs); adaptive resonance theory (ART); flow visualization; particle image velocimetry (PIV); flow velocity; uniform flow; rotating flow; natural convection;
D O I
10.1016/S0954-1810(96)00022-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A novel algorithm for obtaining flow velocity vectors using ART2 networks (based on adaptive resonance theory) is presented. The method involves tracking the movement of groups of seeding particles in a fluid space through the analysis of two successive images. Simulated flows, created artificially by shifting the particles through known distances or rotating through known angles, were used to establish the accuracy of the technique in predicting displacements. Accuracies were quantified by comparison with known displacements and were found to improve with increasing displacement, angle of rotation and size of the sampling window. In addition, the technique has been extended to derive qualitative and quantitative information for a practical case of natural convective flow. (C) 1997 Elsevier Science Ltd.
引用
收藏
页码:135 / 141
页数:7
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